Note

In recognition of the rapid pace of analytical tools and approaches to EWAS and Illumina 450k arrays, we conducted an analysis of our dataset guided by the recommendations of Maksimovic et al. (http://f1000research.com/articles/5-1281/v3). This Rmarkdown document contains the full code used to generate the plots and tables presented.

# package loading
library(data.table)
library(dplyr)
library(limma)
library(minfi)
library(IlluminaHumanMethylation450kanno.ilmn12.hg19)
library(IlluminaHumanMethylation450kmanifest)
library(RColorBrewer)
library(missMethyl)
library(matrixStats)
library(Gviz)
Loading required package: grid
library(DMRcate)
Loading required package: DSS
Loading required package: bsseq
package 'bsseq' was built under R version 3.4.1
Attaching package: 'bsseq'

The following object is masked from 'package:minfi':

    getMeth

Loading required package: splines
Loading required package: DMRcatedata
library(stringr)
library(data.table)
library(dplyr)
sessionInfo()
R version 3.4.0 (2017-04-21)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: OS X El Capitan 10.11.6

Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
 [1] splines   grid      stats4    parallel  stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] stringr_1.2.0                                      DMRcate_1.12.1                                     DMRcatedata_1.12.0                                 DSS_2.16.0                                        
 [5] bsseq_1.12.2                                       Gviz_1.20.0                                        missMethyl_1.10.0                                  RColorBrewer_1.1-2                                
 [9] IlluminaHumanMethylation450kmanifest_0.4.0         IlluminaHumanMethylation450kanno.ilmn12.hg19_0.6.0 BiocInstaller_1.26.1                               minfi_1.22.1                                      
[13] bumphunter_1.16.0                                  locfit_1.5-9.1                                     iterators_1.0.8                                    foreach_1.4.3                                     
[17] Biostrings_2.44.2                                  XVector_0.16.0                                     SummarizedExperiment_1.6.4                         DelayedArray_0.2.7                                
[21] matrixStats_0.52.2                                 Biobase_2.36.2                                     GenomicRanges_1.28.5                               GenomeInfoDb_1.12.2                               
[25] IRanges_2.10.3                                     S4Vectors_0.14.4                                   BiocGenerics_0.22.0                                limma_3.32.6                                      
[29] dplyr_0.7.3                                        data.table_1.10.4                                 

loaded via a namespace (and not attached):
  [1] backports_1.1.0                                     Hmisc_4.0-3                                         AnnotationHub_2.8.2                                 plyr_1.8.4                                         
  [5] lazyeval_0.2.0                                      BiocParallel_1.10.1                                 ggplot2_2.2.1.9000                                  digest_0.6.12                                      
  [9] ensembldb_2.0.4                                     htmltools_0.3.6                                     GO.db_3.4.1                                         magrittr_1.5                                       
 [13] checkmate_1.8.3                                     memoise_1.1.0                                       BSgenome_1.44.1                                     cluster_2.0.6                                      
 [17] annotate_1.54.0                                     R.utils_2.5.0                                       siggenes_1.50.0                                     colorspace_1.3-2                                   
 [21] blob_1.1.0                                          BiasedUrn_1.07                                      RCurl_1.95-4.8                                      genefilter_1.58.1                                  
 [25] bindr_0.1                                           GEOquery_2.42.0                                     survival_2.41-3                                     VariantAnnotation_1.22.3                           
 [29] glue_1.1.1                                          ruv_0.9.6                                           registry_0.3                                        gtable_0.2.0                                       
 [33] zlibbioc_1.22.0                                     scales_0.5.0.9000                                   DBI_0.7                                             rngtools_1.2.4                                     
 [37] Rcpp_0.12.12                                        xtable_1.8-2                                        htmlTable_1.9                                       foreign_0.8-69                                     
 [41] bit_1.1-12                                          mclust_5.3                                          preprocessCore_1.38.1                               Formula_1.2-2                                      
 [45] htmlwidgets_0.9                                     httr_1.3.1                                          acepack_1.4.1                                       pkgconfig_2.0.1                                    
 [49] reshape_0.8.7                                       XML_3.98-1.9                                        R.methodsS3_1.7.1                                   nnet_7.3-12                                        
 [53] rlang_0.1.2                                         AnnotationDbi_1.38.2                                munsell_0.4.3                                       tools_3.4.0                                        
 [57] RSQLite_2.0                                         yaml_2.1.14                                         org.Hs.eg.db_3.4.1                                  knitr_1.17                                         
 [61] bit64_0.9-7                                         beanplot_1.2                                        methylumi_2.22.0                                    AnnotationFilter_1.0.0                             
 [65] bindrcpp_0.2                                        nlme_3.1-131                                        doRNG_1.6.6                                         mime_0.5                                           
 [69] nor1mix_1.2-3                                       R.oo_1.21.0                                         biomaRt_2.32.1                                      compiler_3.4.0                                     
 [73] curl_2.8.1                                          interactiveDisplayBase_1.14.0                       tibble_1.3.4                                        statmod_1.4.30                                     
 [77] stringi_1.1.5                                       GenomicFeatures_1.28.5                              lattice_0.20-35                                     IlluminaHumanMethylationEPICanno.ilm10b2.hg19_0.6.0
 [81] ProtGenerics_1.8.0                                  Matrix_1.2-11                                       permute_0.9-4                                       multtest_2.32.0                                    
 [85] bitops_1.0-6                                        httpuv_1.3.5                                        rtracklayer_1.36.4                                  R6_2.2.2                                           
 [89] latticeExtra_0.6-28                                 gridExtra_2.3                                       codetools_0.2-15                                    dichromat_2.0-0                                    
 [93] MASS_7.3-47                                         gtools_3.5.0                                        assertthat_0.2.0                                    openssl_0.9.7                                      
 [97] pkgmaker_0.22                                       GenomicAlignments_1.12.2                            Rsamtools_1.28.0                                    GenomeInfoDbData_0.99.0                            
[101] quadprog_1.5-5                                      rpart_4.1-11                                        base64_2.0                                          illuminaio_0.18.0                                  
[105] biovizBase_1.24.0                                   shiny_1.0.5                                         base64enc_0.1-3                                    

Pull in annotation, metadata, and raw Illumina 450k data from the idat files

head(detP %>% DT::datatable())
$x
$x$filter
[1] "none"

$x$data

$x$container
[1] "<table class=\"display\">\n  <thead>\n    <tr>\n      <th> </th>\n      <th>A31241</th>\n      <th>A07627</th>\n      <th>A16016</th>\n      <th>A12853</th>\n      <th>A41135</th>\n      <th>A18307</th>\n      <th>B08456</th>\n      <th>B55270</th>\n      <th>B62321</th>\n      <th>A33328</th>\n      <th>B00506</th>\n      <th>B09297</th>\n      <th>B54759</th>\n      <th>A39244</th>\n      <th>B62382</th>\n      <th>A19082</th>\n      <th>B09987</th>\n      <th>A18725</th>\n      <th>A08725</th>\n      <th>A35720</th>\n      <th>B09869</th>\n      <th>B50819</th>\n      <th>B53477</th>\n      <th>B09936</th>\n      <th>B03613</th>\n      <th>B52639</th>\n      <th>B08543</th>\n      <th>B03745</th>\n      <th>A33401</th>\n      <th>A10602</th>\n      <th>A40627</th>\n      <th>A32982</th>\n      <th>A36949</th>\n      <th>A34182</th>\n      <th>B50075</th>\n      <th>B50263</th>\n      <th>B62253</th>\n      <th>B09582</th>\n      <th>B53330</th>\n      <th>A15072</th>\n      <th>A16531</th>\n      <th>B08035</th>\n      <th>A33134</th>\n      <th>B06394</th>\n      <th>B51657</th>\n      <th>A26886</th>\n      <th>A34303</th>\n      <th>B54178</th>\n      <th>B07227</th>\n      <th>A23467</th>\n      <th>B50299</th>\n      <th>A39840</th>\n      <th>A19654</th>\n      <th>B51013</th>\n      <th>A37706</th>\n      <th>A35050</th>\n      <th>B53321</th>\n      <th>A35358</th>\n      <th>B09758</th>\n      <th>A11460</th>\n      <th>A11816</th>\n      <th>A07358</th>\n      <th>A34569</th>\n      <th>B03416</th>\n      <th>B62049</th>\n      <th>B54450</th>\n      <th>A13635</th>\n      <th>A09636</th>\n      <th>A27829</th>\n      <th>B05865</th>\n      <th>B08877</th>\n      <th>A22227</th>\n      <th>B53679</th>\n      <th>A33699</th>\n      <th>A35174</th>\n      <th>B04000</th>\n      <th>A09631</th>\n      <th>A13799</th>\n      <th>B55125</th>\n      <th>A10401</th>\n      <th>B07831</th>\n      <th>B09353</th>\n      <th>B54602</th>\n      <th>A15597</th>\n      <th>A08054</th>\n      <th>A28326</th>\n      <th>B52341</th>\n      <th>B55335</th>\n      <th>A40764</th>\n      <th>B06018</th>\n      <th>B53779</th>\n      <th>A08424</th>\n      <th>B50435</th>\n      <th>B50395</th>\n      <th>B51283</th>\n      <th>A32642</th>\n      <th>A17607</th>\n      <th>A19764</th>\n      <th>B51250</th>\n      <th>A23005</th>\n      <th>A25857</th>\n      <th>B05695</th>\n      <th>A07449</th>\n      <th>B54644</th>\n      <th>B06778</th>\n      <th>B07107</th>\n      <th>A20138</th>\n      <th>B06410</th>\n      <th>B08176</th>\n      <th>B09879</th>\n      <th>A11512</th>\n      <th>A13545</th>\n      <th>A23510</th>\n      <th>A05706</th>\n      <th>B53285</th>\n      <th>A15367</th>\n      <th>A35751</th>\n      <th>B07230</th>\n      <th>A38108</th>\n      <th>B52997</th>\n      <th>A12279</th>\n      <th>B00382</th>\n      <th>A29901</th>\n      <th>A35164</th>\n      <th>A30503</th>\n      <th>B53325</th>\n      <th>B52626</th>\n      <th>B54553</th>\n      <th>A36607</th>\n      <th>A13050</th>\n      <th>A05761</th>\n      <th>B53990</th>\n      <th>B51927</th>\n      <th>B06834</th>\n      <th>A08883</th>\n      <th>B52273</th>\n      <th>A28126</th>\n      <th>B03525</th>\n      <th>A28986</th>\n      <th>B62212</th>\n      <th>B00796</th>\n      <th>B00858</th>\n      <th>B05946</th>\n      <th>B54521</th>\n      <th>A10596</th>\n    </tr>\n  </thead>\n</table>"

$x$options
$x$options$columnDefs
$x$options$columnDefs[[1]]
$x$options$columnDefs[[1]]$className
[1] "dt-right"

$x$options$columnDefs[[1]]$targets
  [1]   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54  55  56
 [57]  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89  90  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107 108 109 110 111 112
[113] 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145


$x$options$columnDefs[[2]]
$x$options$columnDefs[[2]]$orderable
[1] FALSE

$x$options$columnDefs[[2]]$targets
[1] 0



$x$options$order
list()

$x$options$autoWidth
[1] FALSE

$x$options$orderClasses
[1] FALSE

attr(,"escapeIdx")
[1] "true"

$x$selection
$x$selection$mode
[1] "multiple"

$x$selection$selected
NULL

$x$selection$target
[1] "row"


attr(,"colnames")
  [1] " "      "A31241" "A07627" "A16016" "A12853" "A41135" "A18307" "B08456" "B55270" "B62321" "A33328" "B00506" "B09297" "B54759" "A39244" "B62382" "A19082" "B09987" "A18725" "A08725" "A35720" "B09869" "B50819" "B53477" "B09936"
 [26] "B03613" "B52639" "B08543" "B03745" "A33401" "A10602" "A40627" "A32982" "A36949" "A34182" "B50075" "B50263" "B62253" "B09582" "B53330" "A15072" "A16531" "B08035" "A33134" "B06394" "B51657" "A26886" "A34303" "B54178" "B07227"
 [51] "A23467" "B50299" "A39840" "A19654" "B51013" "A37706" "A35050" "B53321" "A35358" "B09758" "A11460" "A11816" "A07358" "A34569" "B03416" "B62049" "B54450" "A13635" "A09636" "A27829" "B05865" "B08877" "A22227" "B53679" "A33699"
 [76] "A35174" "B04000" "A09631" "A13799" "B55125" "A10401" "B07831" "B09353" "B54602" "A15597" "A08054" "A28326" "B52341" "B55335" "A40764" "B06018" "B53779" "A08424" "B50435" "B50395" "B51283" "A32642" "A17607" "A19764" "B51250"
[101] "A23005" "A25857" "B05695" "A07449" "B54644" "B06778" "B07107" "A20138" "B06410" "B08176" "B09879" "A11512" "A13545" "A23510" "A05706" "B53285" "A15367" "A35751" "B07230" "A38108" "B52997" "A12279" "B00382" "A29901" "A35164"
[126] "A30503" "B53325" "B52626" "B54553" "A36607" "A13050" "A05761" "B53990" "B51927" "B06834" "A08883" "B52273" "A28126" "B03525" "A28986" "B62212" "B00796" "B00858" "B05946" "B54521" "A10596"
attr(,"rownames")
[1] TRUE

$width
NULL

$height
NULL

$sizingPolicy
$sizingPolicy$defaultWidth
NULL

$sizingPolicy$defaultHeight
NULL

$sizingPolicy$padding
NULL

$sizingPolicy$viewer
$sizingPolicy$viewer$defaultWidth
NULL

$sizingPolicy$viewer$defaultHeight
NULL

$sizingPolicy$viewer$padding
NULL

$sizingPolicy$viewer$fill
[1] TRUE

$sizingPolicy$viewer$suppress
[1] FALSE

$sizingPolicy$viewer$paneHeight
NULL


$sizingPolicy$browser
$sizingPolicy$browser$defaultWidth
NULL

$sizingPolicy$browser$defaultHeight
NULL

$sizingPolicy$browser$padding
NULL

$sizingPolicy$browser$fill
[1] FALSE


$sizingPolicy$knitr
$sizingPolicy$knitr$defaultWidth
[1] "100%"

$sizingPolicy$knitr$defaultHeight
[1] "auto"

$sizingPolicy$knitr$figure
[1] FALSE



$dependencies
$dependencies[[1]]
List of 10
 $ name      : chr "dt-core"
 $ version   : chr "1.10.12"
 $ src       :List of 1
  ..$ file: chr "/Library/Frameworks/R.framework/Versions/3.4/Resources/library/DT/htmlwidgets/lib/datatables"
 $ meta      : NULL
 $ script    : chr "js/jquery.dataTables.min.js"
 $ stylesheet: chr [1:2] "css/jquery.dataTables.min.css" "css/jquery.dataTables.extra.css"
 $ head      : NULL
 $ attachment: NULL
 $ package   : NULL
 $ all_files : logi FALSE
 - attr(*, "class")= chr "html_dependency"


$elementId
NULL

Check samples for high amounts of poorly performing sites

Generally we want well under 5% of sites failing. Error rates for our data are very low.

barplot(colMeans(detP), col=pal[factor(targets$Sample_Group)], las=2,
        cex.names=0.8, ylab="Mean detection p-values")
abline(h=0.05,col="red")
legend("topleft", legend=levels(factor(targets$Sample_Group)), fill=pal,
       bg="white")
barplot(colMeans(detP), col=pal[factor(targets$Sample_Group)], las=2,
        cex.names=0.8, ylim=c(0,0.002), ylab="Mean detection p-values")

abline(h=0.05,col="red")
legend("topleft", legend=levels(factor(targets$Sample_Group)), fill=pal,
       bg="white")

Normalize data with preprocessQuantile and plot differences in global methylation patterning

# normalize the data; this results in a GenomicRatioSet object
mSetSq <- preprocessQuantile(rgSet)
[preprocessQuantile] Mapping to genome.
[preprocessQuantile] Fixing outliers.
[preprocessQuantile] Quantile normalizing.
# create a MethylSet object from the raw data for plotting
mSetRaw <- preprocessRaw(rgSet)
# visualise what the data looks like before and after normalization
targets <- targets[match(mSetSq$Sample_Name, targets$Sample),]
par(mfrow=c(1,2))
densityPlot(rgSet, sampGroups=targets$Sample_Group,main="Raw", legend=FALSE)
densityPlot(getBeta(mSetSq), sampGroups=targets$Sample_Group,
            main="Normalized", legend=FALSE)

Check for systematic differences in methylation patterning

MDS plotting reveals two distinct groups, which are revealed by labeling for different factors, to be gender

# MDS plots to look at largest sources of variation
# first, line up targets and mSetSq
targets <- targets[match(mSetSq$Sample_Name, targets$Sample),]
par(mfrow=c(2,2))
plotMDS(getM(mSetSq), top=1000, gene.selection="common",
        col=pal[factor(targets$Sample_Group)])
legend("top", legend=levels(factor(targets$Sample_Group)), text.col=pal,
       bg="white", cex=0.7)
plotMDS(getM(mSetSq), top=1000, gene.selection="common",
        col=pal[factor(targets$Gender)])
legend("top", legend=levels(factor(targets$Gender)), text.col=pal,
       bg="white", cex=0.7)
plotMDS(getM(mSetSq), top=1000, gene.selection="common",
        col=pal[factor(targets$Ethnicity)])
legend("top", legend=levels(factor(targets$Ethnicity)), text.col=pal,
       bg="white", cex=0.7)
plotMDS(getM(mSetSq), top=1000, gene.selection="common",
        col=pal[factor(targets$Sample_Plate)])
legend("top", legend=levels(factor(targets$Sample_Plate)), text.col=pal,
       bg="white", cex=0.7)

Replot MDS with probes removed

# MDS plots to look at largest sources of variation
# now with the filtered set
# again, ensure targets and mSetSqFlt line up
targets <- targets[match(mSetSqFlt$Sample_Name, targets$Sample),]
par(mfrow=c(2,2))
plotMDS(getM(mSetSqFlt), top=1000, gene.selection="common",
        col=pal[factor(targets$Sample_Group)])
legend("top", legend=levels(factor(targets$Sample_Group)), text.col=pal,
       bg="white", cex=0.7)
plotMDS(getM(mSetSqFlt), top=1000, gene.selection="common",
        col=pal[factor(targets$Gender)])
legend("top", legend=levels(factor(targets$Gender)), text.col=pal,
       bg="white", cex=0.7)
plotMDS(getM(mSetSqFlt), top=1000, gene.selection="common",
        col=pal[factor(targets$Ethnicity)])
legend("top", legend=levels(factor(targets$Ethnicity)), text.col=pal,
       bg="white", cex=0.7)
plotMDS(getM(mSetSqFlt), top=1000, gene.selection="common",
        col=pal[factor(targets$Sample_Plate)])
legend("top", legend=levels(factor(targets$Sample_Plate)), text.col=pal,
       bg="white", cex=0.7)

Calculate M-values for statistical analysis and Beta values for plotting

# calculate M-values for statistical analysis
mVals <- getM(mSetSqFlt)
#head(mVals[,1:5])
bVals <- getBeta(mSetSqFlt)
#head(bVals[,1:5])

Use limma with a linear model and eBayes for significant testing to find differentially methylated positions

The approach used in the manuscript

summary(decideTests(fit2))
   Case-Control
-1           19
0        360931
1           101
# get the table of results for the first contrast 
ann450kSub <- ann450k[match(rownames(mVals),ann450k$Name),
                      c(1:4,12:19,24:ncol(ann450k))]
DMPs <- topTable(fit2, num=Inf, coef=1, genelist=ann450kSub)
head(DMPs, n=50)
# get beta values 
bVals <- getBeta(mSetSq)
# plot the top 16 most significantly differentially methylated CpGs
par(mfrow=c(4,4))
sapply(rownames(DMPs)[1:16], function(cpg){
  plotCpg(bVals, cpg=cpg, pheno=targets$Sample_Group, ylab = "Beta values")
})
$cg09249404
NULL

$cg26250086
NULL

$cg16092017
NULL

$cg18901140
NULL

$cg09935388
NULL

$cg00636368
NULL

$cg06168149
NULL

$cg09256832
NULL

$cg21390082
NULL

$cg11067714
NULL

$cg00058163
NULL

$cg05567511
NULL

$cg01257889
NULL

$cg22623319
NULL

$cg18146737
NULL

$cg03775416
NULL

Differential methylation analysis of regions

The probe-wise test did not find much of anything, so let’s try the dmrcate package to find regions of differential methylation

I plot the top hit, which looks virtually identical between case and control

Differential variability

Test for difference in variance between groups

Nothing looks interesting

par(mfrow=c(4,4))
sapply(rownames(topDV)[1:10], function(cpg){
  plotCpg(bVals, cpg=cpg, pheno=targets$Sample_Group,
       ylab = "Beta values")
})
$cg14361458
NULL

$cg14501061
NULL

$cg12821876
NULL

$cg00110609
NULL

$cg05756220
NULL

$cg12936469
NULL

$cg00879475
NULL

$cg06342195
NULL

$cg26117025
NULL

$cg02049404
NULL

Conclusion

Largely the same results I believe. But reviewers shouldn’t have any issue with the analysis pipeline.

---
title: Reanalysis of DNA methylation analyses in archived newborn bloodspots reveal
  signals associated with fetal alcohol syndrome, maternal smoking, gender, and ethnicity
author: "David McGaughey"
date: "July 7, 2016 | September 22, 2017"
output: 
  html_notebook:
    toc: true
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

## Note

In recognition of the rapid pace of analytical tools and approaches to EWAS and Illumina 450k arrays, we conducted an analysis of our dataset guided by the recommendations of Maksimovic et al. (http://f1000research.com/articles/5-1281/v3). This Rmarkdown document contains the full code used to generate the plots and tables presented. 

```{r}
# package loading
library(data.table)
library(dplyr)
library(limma)
library(minfi)
library(IlluminaHumanMethylation450kanno.ilmn12.hg19)
library(IlluminaHumanMethylation450kmanifest)
library(RColorBrewer)
library(missMethyl)
library(matrixStats)
library(Gviz)
library(DMRcate)
library(stringr)
library(data.table)
library(dplyr)

sessionInfo()
```

## Pull in annotation, metadata, and raw Illumina 450k data from the idat files

```{r}
# pull annotation
ann450k <-  getAnnotation(IlluminaHumanMethylation450kanno.ilmn12.hg19)

# bring in raw data (Illumina .idat)
# data and code available at:
#   https://github.com/davemcg/fetal_alcohol
#   GSE65428
metadata <- fread('~/git/fetal_alcohol/sentrix_id_conversion_table_demographics_v6.txt')
targets <- read.metharray.sheet("/Volumes/ThunderBay/PROJECTS/brody/fetal_alcohol/Tsai_Sample_083/",pattern="Sheet.csv$")
rgSet <- read.metharray.exp(targets=targets)
sampleNames(rgSet) <- rgSet$Sample_Name

# add sample group info to targets, fewer rows in metadata, already parsed down to remove outliers
targets <- left_join(data.frame(metadata),targets,by=c("Sample"="Sample_Name"))
targets$Sample_Group <- targets$Case.Control
targets$Sample_Name <- targets$Sample

# remove samples based on previous outlier analysis / ethnicity (see Methods of "DNA methylation analyses in archived newborn bloodspots reveal signals associated with fetal alcohol syndrome, maternal smoking, gender, and ethnicity")
keep <-  rgSet$Sample_Name %in% targets$Sample_Name
rgSet <- rgSet[,keep]

# calculate the detection p-values for poorly performing 450k probes
detP <- detectionP(rgSet)
head(detP %>% DT::datatable())

```

## Check samples for high amounts of poorly performing sites 

Generally we want well under 5% of sites failing. Error rates for our data are very low.

```{r, fig.height=2}
# plot detection p-values
# everything looks fine in this data-set
# first match up targets to detP
targets <- targets[match(colnames(detP), targets$Sample),]

pal <- brewer.pal(8,"Dark2")

barplot(colMeans(detP), col=pal[factor(targets$Sample_Group)], las=2,
        cex.names=0.8, ylab="Mean detection p-values")
abline(h=0.05,col="red")
legend("topleft", legend=levels(factor(targets$Sample_Group)), fill=pal,
       bg="white")

barplot(colMeans(detP), col=pal[factor(targets$Sample_Group)], las=2,
        cex.names=0.8, ylim=c(0,0.002), ylab="Mean detection p-values")
abline(h=0.05,col="red")
legend("topleft", legend=levels(factor(targets$Sample_Group)), fill=pal,
       bg="white")

```


## Normalize data with preprocessQuantile and plot differences in global methylation patterning

```{r, fig.width=2}
# normalize the data; this results in a GenomicRatioSet object
mSetSq <- preprocessQuantile(rgSet)

# create a MethylSet object from the raw data for plotting
mSetRaw <- preprocessRaw(rgSet)
# visualise what the data looks like before and after normalization
targets <- targets[match(mSetSq$Sample_Name, targets$Sample),]
par(mfrow=c(1,2))
densityPlot(rgSet, sampGroups=targets$Sample_Group,main="Raw", legend=FALSE)
densityPlot(getBeta(mSetSq), sampGroups=targets$Sample_Group,
            main="Normalized", legend=FALSE)
```

## Check for systematic differences in methylation patterning

MDS plotting reveals two distinct groups, which are revealed by labeling for different factors, to be gender

```{r}

# MDS plots to look at largest sources of variation
# first, line up targets and mSetSq
targets <- targets[match(mSetSq$Sample_Name, targets$Sample),]

par(mfrow=c(2,2))
plotMDS(getM(mSetSq), top=1000, gene.selection="common",
        col=pal[factor(targets$Sample_Group)])
legend("top", legend=levels(factor(targets$Sample_Group)), text.col=pal,
       bg="white", cex=0.7)

plotMDS(getM(mSetSq), top=1000, gene.selection="common",
        col=pal[factor(targets$Gender)])
legend("top", legend=levels(factor(targets$Gender)), text.col=pal,
       bg="white", cex=0.7)

plotMDS(getM(mSetSq), top=1000, gene.selection="common",
        col=pal[factor(targets$Ethnicity)])
legend("top", legend=levels(factor(targets$Ethnicity)), text.col=pal,
       bg="white", cex=0.7)

plotMDS(getM(mSetSq), top=1000, gene.selection="common",
        col=pal[factor(targets$Sample_Plate)])
legend("top", legend=levels(factor(targets$Sample_Plate)), text.col=pal,
       bg="white", cex=0.7)
```

## Remove probes that failed QC (detP), that are related to SNP variation, and have cross-reactivity/binding

```{r}

# ensure probes are in the same order in the mSetSq and detP objects
detP <- detP[match(featureNames(mSetSq),rownames(detP)),]
# remove any probes that have failed in one or more samples
keep <- rowSums(detP < 0.01) == ncol(mSetSq)
table(keep)
mSetSqFlt <- mSetSq[keep,]
mSetSqFlt

# if your data includes males and females, remove probes on the sex chromosomes
keep <- !(featureNames(mSetSqFlt) %in% ann450k$Name[ann450k$chr %in%  c("chrX","chrY")])
mSetSqFlt <- mSetSqFlt[keep,]
table(keep)

# pull in my cg sites that I'm skipping due to overlap with SNPs and 
# are cross-reactive and are on chrs X Y
cg_remove_1 <- scan('~/git/fetal_alcohol/450k_cg_sites_that_LITERALLY_are_on_SNPS.maf005_somepop_plus1bp.txt',what='character')
cg_remove_2 <- scan('~/git/fetal_alcohol/450k_cg_sites_that_overlap_dbsnp138.maf005_somepop_plus1bp.txt',what='character')
xReactive <- scan('~/git/fetal_alcohol/illumina450k_positions_to_exclude.not_including_dbsnp_overlapping.withChen.dat',what='character')
cg_remove <- c(cg_remove_1, cg_remove_2, xReactive)
cg_remove <- unique(cg_remove)

keep <- !(featureNames(mSetSqFlt) %in% cg_remove)
table(keep)
mSetSqFlt <- mSetSqFlt[keep,]           
```

## Replot MDS with probes removed

```{r}
# MDS plots to look at largest sources of variation
# now with the filtered set
# again, ensure targets and mSetSqFlt line up
targets <- targets[match(mSetSqFlt$Sample_Name, targets$Sample),]
par(mfrow=c(2,2))
plotMDS(getM(mSetSqFlt), top=1000, gene.selection="common",
        col=pal[factor(targets$Sample_Group)])
legend("top", legend=levels(factor(targets$Sample_Group)), text.col=pal,
       bg="white", cex=0.7)

plotMDS(getM(mSetSqFlt), top=1000, gene.selection="common",
        col=pal[factor(targets$Gender)])
legend("top", legend=levels(factor(targets$Gender)), text.col=pal,
       bg="white", cex=0.7)

plotMDS(getM(mSetSqFlt), top=1000, gene.selection="common",
        col=pal[factor(targets$Ethnicity)])
legend("top", legend=levels(factor(targets$Ethnicity)), text.col=pal,
       bg="white", cex=0.7)

plotMDS(getM(mSetSqFlt), top=1000, gene.selection="common",
        col=pal[factor(targets$Sample_Plate)])
legend("top", legend=levels(factor(targets$Sample_Plate)), text.col=pal,
       bg="white", cex=0.7)
```

## Calculate M-values for statistical analysis and Beta values for plotting

```{r}

# calculate M-values for statistical analysis
mVals <- getM(mSetSqFlt)
#head(mVals[,1:5])

bVals <- getBeta(mSetSqFlt)
#head(bVals[,1:5])
```

## Use limma with a linear model and eBayes for significant testing to find differentially methylated positions

The approach used in the manuscript
```{r, fig.height=4}
# explicitly match targets sample order (rows) with mVars
targets <- targets[match(colnames(mVals), targets$Sample),]


case_control <- factor(targets$Case.Control)
ethnicity <- factor(targets$Ethnicity)
gender <- factor(targets$Gender)
#education <- as.numeric(targets$Education)
cd8t <- as.numeric(targets$CD8T)
cd4t <- as.numeric(targets$CD4T)
nk <- as.numeric(targets$NK)
mono <- as.numeric(targets$Mono)
gran <- as.numeric(targets$Gran)
smoking <- factor(targets$Smoking)

design <- model.matrix(~0+case_control+gender+cd8t+cd4t+nk+mono+gran+ethnicity, data=targets)
colnames(design)<-c("Case","Control","Gender","CD8T","CD4","NK","MONO","GRAN","Hispanic","Other","White")
cmtx <- makeContrasts( "Case-Control", levels=design)
fit <- lmFit(mVals, design)
fit2 <- contrasts.fit(fit, cmtx)
fit2 <- eBayes(fit2)
summary(decideTests(fit2))


# get the table of results for the first contrast 
ann450kSub <- ann450k[match(rownames(mVals),ann450k$Name),
                      c(1:4,12:19,24:ncol(ann450k))]
DMPs <- topTable(fit2, num=Inf, coef=1, genelist=ann450kSub)
head(DMPs, n=50)

# get beta values 
bVals <- getBeta(mSetSq)

# plot the top 16 most significantly differentially methylated CpGs
par(mfrow=c(4,4))
sapply(rownames(DMPs)[1:16], function(cpg){
  plotCpg(bVals, cpg=cpg, pheno=targets$Sample_Group, ylab = "Beta values")
})
```

## Differential methylation analysis of regions
The probe-wise test did not find much of anything, so let's try the dmrcate package to find regions of differential methylation

I plot the top hit, which looks virtually identical between case and control
```{r, fig.height=10}
myAnnotation <- cpg.annotate(object = mVals, datatype = "array", what = "M",
                                analysis.type= "differential", design = design,
                                contrasts = TRUE, cont.matrix = cmtx,
                                coef = "Case-Control", arraytype = "450K")

DMRs <- dmrcate(myAnnotation, lambda=1000, C=2)
head(DMRs$results, n=50)

# convert the regions to annotated genomic ranges
results.ranges <- extractRanges(DMRs, genome = "hg19")

# set up the grouping variables and colours
groups <- pal[1:length(unique(targets$Sample_Group))]
names(groups) <- levels(factor(targets$Sample_Group))
cols <- groups[as.character(factor(targets$Sample_Group))]
samps <- 1:nrow(targets)
# draw the plot for the top DMR
par(mfrow=c(1,1))
DMR.plot(ranges=results.ranges, dmr=1, CpGs=bVals, phen.col=cols, what = "Beta",
          arraytype = "450K", pch=16, toscale=TRUE, plotmedians=TRUE, 
          genome="hg19", samps=samps)
```

## Differential variability
Test for difference in variance between groups

Nothing looks interesting
```{r, fig.height=3}
fitvar <- varFit(mVals, design = design, coef = c(1,2))
summary(decideTests(fitvar))

DMPs_var <- topTable(fitvar, num=Inf, coef=1, genelist=ann450kSub)
head(DMPs_var, n=20)

topDV <- topVar(fitvar, coef=1)

par(mfrow=c(4,4))
sapply(rownames(topDV)[1:10], function(cpg){
  plotCpg(bVals, cpg=cpg, pheno=targets$Sample_Group,
	   ylab = "Beta values")
})
```

## Conclusion
Largely the same results I believe. But reviewers shouldn't have any issue with the analysis pipeline. 
